Search Results (12,592)

This study employs Design Builder software to evaluate advanced glazing technologies for enhancing the thermal performance of residential buildings in Jeddah, Saudi Arabia. Recognizing the energy inefficiencies caused by adopting Western architectural styles unsuited to local climatic conditions, and given that buildings consume 44% of national energy, we conducted a systematic parametric analysis to isolate the effects of key glazing parameters. The study examines six polycarbonate (PC) configurations and three critical comparative cases: (1) a selective double-glazed unit representing a new baseline glazing; (2) a low-U configuration to isolate thermal insulation effects; and (3) a low-SHGC configuration to evaluate solar heat gain mitigation independently. These controlled comparisons address a critical research gap by decoupling the traditionally confounded impacts of U-value and SHGC in hot climates. The simulations reveal that the 36 mm aerogel glazing (U = 0.9 W/m²·K, SHGC = 0.3) reduces cooling demand by 48.6% annually compared to single-pane glazing while maintaining indoor temperatures at 30.09 °C versus 38.43 °C at baseline. Notably, the findings demonstrate that 87% of these savings derive from SHGC reduction, with only 3.02 percentage points attributable to U-value improvements. The selective DGU benchmark delivers 85% of aerogel’s benefits at 40% lower cost, establishing it as a practical solution for most applications. These findings provide evidence-based guidance for Saudi Vision 2030’s sustainability goals, emphasizing that while aerogel glazing excels in extreme solar exposures, strategic SHGC optimization in conventional glazing can achieve the most energy savings in hot climates. Full article

With the widespread adoption of smartphones, concerns about increased exposure to non-ionizing radiofrequency have emerged. This scoping review examines the effects of mobile phone exposure on neural oscillations and cortical excitability, focusing on both motor and non-motor regions of the cerebral cortex. A scoping review identified seventy-eight studies that involved healthy individuals and employed electroencephalography and only two studies that investigated transcranial magnetic stimulation as primary technical tools. The findings suggest that mobile phone exposure may affect brain oscillations and cortical excitability. However, inconsistencies in experimental methods across studies make it difficult to draw definitive conclusions. Additionally, research on fifth-generation technology, particularly mmWave exposure from next-generation mobile networks, remains limited and needs further exploration. These gaps highlight the need for more in-depth studies on how mobile phone exposure impacts brain function. Full article

Adaptive optics (AO)-corrected retina flood illumination imaging technology is widely used for investigating both structural and functional aspects of the retina. Given the inherent low-contrast nature of original retinal images, it is necessary to perform image restoration. Total variation (TV) regularization is an efficient regularization technique for AO retinal image restoration. However, a main shortcoming of TV regularization is its potential to experience the staircase effects, particularly in smooth regions of the image. To overcome the drawback, a new image restoration model is proposed for AO retinal images. This model utilizes the overlapping group sparse total variation (OGSTV) as a regularization term. Due to the structural characteristics of AO retinal images, only partial information regarding the PSF is known. Consequently, we have to solve a more complicated myopic deconvolution problem. To address this computational challenge, we propose an ADMM-MM-LAP method to solve the proposed model. First, we apply the alternating direction method of multiplier (ADMM) as the outer-layer optimization method. Then, appropriate algorithms are employed to solve the ADMM subproblems based on their inherent structures. Specifically, the majorization–minimization (MM) method is applied to handle the asymmetry OGSTV regularization component, while a modified version of the linearize and project (LAP) method is adopted to address the tightly coupled subproblem. Theoretically, we establish the complexity analysis of the proposed method. Numerical results demonstrate that the proposed model outperforms the existing state-of-the-art TV model across several metrics. Full article

Ground-based interferometric radar (GBIR) is a powerful remote sensing technique used for infrastructure monitoring, particularly in the field of bridge structural health monitoring (SHM). Despite its high resolution and rapid data acquisition and the availability of various commercial systems, GBIR has not yet been fully recognised or routinely adopted in standard bridge monitoring practices. This study presents a comprehensive review of GBIR technologies and methods historically applied in bridge SHM. A total of 104 peer-reviewed papers were selected through a systematic review process, encompassing 128 monitored bridges assessed using a wide range of GBIR systems. The applications of GBIR across different bridge materials and operational conditions are discussed in detail. The review shows that 76% of GBIR applications focus on roadway and railway bridges. In terms of materials, steel and concrete bridges dominate the dataset, accounting for 95% of the total, while masonry bridges represent only 5%. The GBIR system types examined in this study are categorised into six main groups based on their technical specifications, with their key characteristics and capabilities analysed. The review also investigates bridge feature extraction techniques, revealing a predominant focus on identifying natural frequencies, while fewer studies explore the extraction of damping ratios and structural mode shapes. Furthermore, the integration of GBIR with other sensing technologies—particularly accelerometers—is explored, highlighting opportunities for complementary sensor fusion. Overall, this study provides a comprehensive overview of the current state of practice and identifies key areas for future research and technological development. Full article

Empowered by artificial intelligence and 5G technologies, intelligent messaging service instead of the existing short messaging service could provide an omni-channel service, thus achieving higher interconnection for mobile users. In this paper, we adopted mixed methods research and explored the psychological factors that affect adoption intention to adopt intelligent messaging services among mobile users based on self-determination theory. After semi-structured interviews, we constructed a partial least squares structural equation model from the perspectives of intrinsic and extrinsic motivations. In addition, openness and perceived complexity were also introduced as an extended dimension. Through an online survey, 548 valid questionnaires were obtained. The results show that intrinsic motivation has a greater effect on adoption intention. Specifically, attitude, perceived autonomy, perceived relatedness, and perceived system quality have significant positive impacts on the adoption intention of intelligent messaging, while perceived complexity has a negative direct impact on adoption intention. Although perceived competence and perceived media richness have no significant effects on adoption intention, an indirect effect on adoption intention through attitude was observed. Notably, perceived interactivity and openness have no effect on adoption intention. Through this study, we aim to provide guidance for developers to focus on mobile users’ psychological needs regarding upgraded interactive channels, which can accelerate the construction of an omni-channel media environment. Full article

Marine ecosystems are vital for maintaining biodiversity and ecological balance. However, these ecosystems face severe threats from habitat destruction, pollution, climate change, and overfishing. Addressing these challenges requires innovative solutions, including the adoption of marine intelligent technologies. This study examines the role of marine intelligent technologies in promoting ocean sustainability. By integrating bibliometric and trend analyses of 777 publications (2020–2024), the study identifies critical research directions and disparities in the application of these technologies across marine ecosystems, shipping, and fisheries. Key findings reveal that marine intelligent technologies have transformative potential, enabling real-time marine environmental monitoring, enhancing port operations, and reducing the ecological footprints of fisheries. The study highlights the importance of collaborative efforts in policy formulation, technological advancement, and global cooperation to achieve the United Nations’ Sustainable Development Goal 14. Insights from this research provide feasible pathways for aligning technological innovation with the sustainable management of marine resources. Full article

The residential sector is energy-consuming and one of the biggest contributors to climate change. In France, the adoption of photovoltaics (PV) in that sector is accelerating, which contributes to both increasing energy efficiency and reducing greenhouse gas (GHG) emissions, even though the technology faces several issues. One issue that slows down the adoption of the technology is the “duck curve” effect, which is defined as the daily variation of net load derived from a mismatch between power consumption and PV power generation periods. As a possible solution for addressing this issue, electric water heaters (EWHs) can be used in residential building as a means of storing the PV power generation surplus in the form of heat in a context where users’ comfort—the availability of domestic hot water (DHW)—has to be guaranteed. Thus, the present work deals with developing model-based predictive control (MPC) strategies—nonlinear/linear MPC (MPC/LMPC) strategies are proposed—to the management of EWHs in individual dwellings equipped with grid-connected PV systems. The aim behind developing such strategies is to improve both the PV power generation self-consumption rate and the economic gain, in comparison with rule-based (RB) control strategies. Inasmuch as DHW and power demand profiles are needed, data were collected from a panel of users, allowing the development of profiles based on a quantile regression (QR) approach. The simulation results (over 6 days) highlight that the MPC/LMPC strategies outperform the RB strategies, while guaranteeing users’ comfort (i.e., the availability of DHW). The MPC/LMPC strategies allow for a significant increase in both the economic gain (up to 2.70EUR) and the PV power generation self-consumption rate (up to 14.30$\mathrm{p}$s), which in turn allows the ${\mathrm{CO}}_2\mathrm{e}$missions to be reduced (up to 3.92 $\mathrm{k}\mathrm{g}{\mathrm{CO}}_{2\mathrm{eq}})$. In addition, these results clearly demonstrate the benefits of using EWHs to store the PV power generation surplus, in the context of producing DHW in residential buildings. Full article

The great success of fully mechanized mining with a large mining height of 8 m and above in Mongolia and Shaanxi proves the feasibility of this technology. Although fully mechanized mining with a large mining height significantly increases the production capacity of mining areas, there are higher requirements for mining safety. Due to the special conditions of the 8 m coal seam in Huoxi Coalfield, the adoption of fully mechanized mining with a large mining height faces a serious problem of spalling. The key to whether complete equipment for fully mechanized mining with a large mining height can be adopted lies in the possibility to reduce such risks. In this paper, the mechanism of spalling on the 8 m fully mechanized mining face in Huoxi Coalfield is analyzed through theoretical analysis, numerical calculations and comparison of on-site data, and a “one point four zone” comprehensive evaluation method for coal seam at the front end of the working face is established based on the failure characteristics of the coal seam; according to the numerical simulation test results, it is believed that the presence of a dirt band in the middle and upper parts of the coal seam has a positive effect on the control of spalling; the overburden structure of Huoxi Coalfield exhibits the changes in “cantilever-instability-hinge”, and there is a risk of caving before the mining face, which exacerbates spalling; the advance blasting roof cutting of the working face has a significant effect on controlling the spalling of production coal faces. This research provides a theoretical basis and engineering reference for the treatment of spalling in fully mechanized mining with a large mining height in Huoxi Coalfield. Full article

Small and medium-sized enterprises (SMEs) continue to face significant cybersecurity challenges due to limited financial resources, technical capacity, and awareness. This study addresses these issues by pursuing four key objectives: (1) conducting a comprehensive assessment of cybersecurity knowledge and awareness within the SME sector through a systematic literature review, (2) evaluating the impact and effectiveness of cybersecurity awareness programs on SME behaviors and risk mitigation, (3) identifying core barriers—financial, technical, and organizational—that hinder effective cybersecurity adoption, and (4) introducing and validating the enhanced ROHAN model in conjunction with the Cyber Guardian Framework (CGF) to offer a scalable roadmap for cybersecurity resilience. Drawing on secondary data from Rawindaran (2023), the research highlights critical deficiencies in SME cybersecurity practices and emphasizes the need for tailored role-specific awareness initiatives. The enhanced ROHAN model addresses this need by delivering customized cybersecurity education based on industry sector, professional role, and educational background. Integrated with the CGF, the framework promotes structured, ongoing improvements across organizational, technological, and human domains. A mixed-methods approach was used, combining quantitative survey data from Welsh SMEs with qualitative interviews involving SME stakeholders. Advanced analytical techniques, including regression testing, Principal Component Analysis (PCA), and data visualization, were employed to uncover key insights and patterns. A distinctive feature of the ROHAN model is its integration of AI-powered tools for real-time risk assessment and decision-making, reflecting the principles of Industry 5.0. By aligning technological innovation with targeted education, this study presents a practical and adaptable cybersecurity framework for SMEs. The findings aim to bridge critical knowledge gaps and provide a foundation for a more resilient, cyber-aware SME sector in Wales and comparable regions. Full article

Coconut (Cocos nucifera L.) is a nutrient-rich plant extensively cultivated in tropical and subtropical regions. Coconut water (CW), the primary edible component of the fruit, has gained significant attention due to its nutritional value and increasing popularity as a functional beverage. In addition to its hydrating properties, CW is rich in essential nutrients such as sugars, minerals, and vitamins, which contribute to its diverse biological activities, including antioxidant, anti-inflammatory, anti-cancer, cardioprotective, and antimicrobial effects. However, CW’s high perishability and susceptibility to rapid deterioration present significant challenges for its preservation. The growing demand for natural and fresh CW has driven the development of innovative technologies aiming at extending its shelf life while maintaining its nutritional quality and sensory attributes. This review highlights recent research advancements in CW, focusing on its nutritional composition, biological activities, and innovations in preservation technologies. The aim is to facilitate the optimization of CW beverage formulations, promote the adoption of effective preservation methods, and drive the development of high-quality and consumer-appealing CW products. Full article

Rammed earth (RE), despite being an ancient method of construction, has smoothly integrated into contemporary civil engineering due to its compatibility with current sustainability requirements for housing structures. However, typical RE needs some improvements to fully realize its potential as both a structurally effective and environmentally friendly building technique. As a result, multiple bio-inspired enhancement methods have been suggested to substitute traditional cement or lime stabilizers. This review examines the various efforts made in the past decade to biologically stabilize natural soil for the construction of RE. It provides a brief overview of the different bio-based materials utilized in this area but primarily concentrates on their effects on the mechanical strength and water durability of RE structures. The review also addresses current obstacles that prevent the widespread industrial adoption of this valuable earth-building method and identifies potential directions for future research. Overall, the available literature on the mechanical performance and durability of bio-based rammed earth (BRE) shows encouraging outcomes. Nonetheless, various issues, such as the absence of thorough data on the discussed topics, issues related to the inherent properties of soil and biomaterials, and doubts regarding the reliability of durability evaluation methods, have been identified as factors that could lead to a lack of confidence among RE practitioners in adopting bio-based treatments. This study will provide a solid foundation for future researchers aiming to advance BRE technology, thus enhancing sustainability within the construction sector. Full article

Financial technology (FinTech) rapidly transforms financial landscapes across ASEAN-4 countries by enhancing financial inclusion and digital service accessibility. However, the key factors driving FinTech development in these economies remain ambiguous. While existing studies highlight the economic and technological aspects of FinTech adoption, limited research distinguishes the unique conditions shaping FinTech’s evolution in developing ASEAN markets. This study bridges this gap by identifying economic and non-economic determinants and exploring their mediating effects. This research aims to investigate the primary drivers of FinTech development in ASEAN-4, emphasizing the roles of financial access and technological readiness as mediators in fostering a sustainable FinTech ecosystem. Utilizing structural equation modeling (SEM) with SmartPLS3, this study analyzes secondary data from 2008 to 2018, evaluating macroeconomic indicators, banking conditions, internet penetration, innovation levels, population dynamics, and human development factors. General banking conditions, access to finance, and technological readiness significantly impact FinTech development. Additionally, financial accessibility and technological infrastructure mediate the influence of economic stability, innovation, and digital penetration on FinTech growth. This study underscores policymakers’ and stakeholders’ need to enhance digital infrastructure and financial accessibility to accelerate FinTech growth. Strengthening financial ecosystems will drive digital transformation and economic resilience in emerging ASEAN economies. Full article

Background: The objective of this qualitative research was to explore the impact of certain predefined factors on the sustainability of supply chains in Industry 5.0. The sustainability of supply chains was assessed by considering six factors from the existing literature: circular logistics, Industry 5.0 technologies, intellectual level and learning, logistics innovation, sustainable supply chain practices, and the managerial strategy of the company. Methods: Semi-structured interviews were conducted based on an interview guide with 14 participants who serve as managers and executives in supply chain management in Morocco. The interviews were transcribed, followed by lexical and thematic analysis using QSR NVivo 15 software. Results: The results showed that all the factors studied play a major role in achieving sustainable supply chain management. The testimonies of all participants highlighted the crucial role that Industry 5.0 technologies play in achieving the sustainability of supply chains, while combining them with the other factors studied. Consequently, these results made it possible to validate and confirm the six factors previously identified from the existing literature, while retaining them for potential future quantitative studies. Conclusions: To meet the dimensions of sustainability, it is recommended to focus on the most impactful factors in maintaining a sustainable supply chain and on all the possible interactions between these factors. Indeed, this study represents, on the one hand, an invitation for supply chain managers to pay greater attention to factors related to sustainability, and on the other hand, an initial pathway for future research that may subsequently highlight the interactions between factors leading to sustainability. This could help propose applicable models for supply chain managers, enabling them to adopt more effective strategies and identify the most feasible combinations of factors to achieve sustainable logistics. Full article

RSSI-based proximity positioning is a well-established technique for indoor localization, featuring simplicity and cost-effectiveness, requiring low-price and off-the-shelf hardware. However, it suffers from low accuracy (in NLOS traffic), noise, and multipath fading issues. In large complex spaces, such as museums, where heavy visitor traffic is expected to seriously impact the ability to maintain LOS, RSSI coupled with Bluetooth Low Energy (BLE) seems ideal in terms of market availability, cost-/energy-efficiency and scalability that affect competing technologies, provided it achieves adequate accuracy. Our work reports and discusses findings of a BLE/RSSI-based pilot, implemented at the Museum of Modern Greek Culture in Athens, involving eight buildings with 47 halls with diverse areas, shapes, and showcase layouts. Wearable visitor BLE beacons provided cell-level location determined by a prototype tool (VTT), integrating in its architecture different functionalities: raw RSSI data smoothing with Kalman filters, hybrid positioning provision, temporal methods for visitor cell prediction, spatial filtering, and prediction based on popular machine learning classifiers. Visitor movement modeling, based on critical parameters influencing signal measurements, provided scenarios mapped to popular behavioral models. One such model, “ant”, corresponding to relatively slow nomadic cell roaming, was selected for basic experimentation. Pilot implementation decisions and methods adopted at all layers of the VTT architecture followed the overall concept of simplicity, availability, and cost-efficiency, providing a maximum infrastructure cost of 8 Euro per m² covered. A total 15 methods/algorithms were evaluated against prediction accuracy across 20 RSSI datasets, incorporating diverse hall cell allocations and visitor movement patterns. RSSI data, temporal and spatial management with simple low-processing methods adopted, achieved a maximum prediction accuracy average of 81.53% across all datasets, while ML algorithms (Random Forest) achieved a maximum prediction accuracy average of 87.24%. Full article

The advancement of precision agriculture increasingly depends on innovative technological solutions that optimize resource utilization and minimize environmental impact. This paper introduces a novel heterogeneous federated learning architecture specifically designed for intelligent agricultural systems, with a focus on combine tractors equipped with advanced nutrient and crop health sensors. Unlike conventional FL applications, our architecture uniquely addresses the challenges of communication efficiency, dynamic network conditions, and resource allocation in rural farming environments. By adopting a decentralized approach, we ensure that sensitive data remain localized, thereby enhancing security while facilitating effective collaboration among devices. The architecture promotes the formation of adaptive clusters based on operational capabilities and geographical proximity, optimizing communication between edge devices and a global server. Furthermore, we implement a robust checkpointing mechanism and a dynamic data transmission strategy, ensuring efficient model updates in the face of fluctuating network conditions. Through a comprehensive assessment of computational power, energy efficiency, and latency, our system intelligently classifies devices, significantly enhancing the overall efficiency of federated learning processes. This paper details the architecture, operational procedures, and evaluation methodologies, demonstrating how our approach has the potential to transform agricultural practices through data-driven decision-making and promote sustainable farming practices tailored to the unique challenges of the agricultural sector. Full article